Semantic-enhanced models to support timely admission prediction at emergency departments

Li, Jiexun; Guo, Lifan; Handly, Neal; Aline, A.; Thompson, David A.

doi:10.1007/s13721-012-0014-6

Cited by 5 publications

References 42 publications

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A cost sensitive inpatient bed reservation approach to reduce emergency department boarding times

Qiu

Chinnam

Murat

et al. 2014

Health Care Manag Sci

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Emergency departments (ED) in hospitals are experiencing severe crowding and prolonged patient waiting times. A significant contributing factor is boarding delays where admitted patients are held in ED (occupying critical resources) until an inpatient bed is identified and readied in the admit wards. Recent research has suggested that if the hospital admissions of ED patients can be predicted during triage or soon after, then bed requests and preparations can be triggered early on to reduce patient boarding time. We propose a cost sensitive bed reservation policy that recommends optimal bed reservation times for patients. The policy relies on a classifier that estimates the probability that the ED patient will be admitted using the patient information collected and readily available at triage or right after. The policy is cost sensitive in that it accounts for costs associated with patient admission prediction misclassification as well as costs associated with incorrectly selecting the reservation time. Results from testing the proposed bed reservation policy using data from a VA Medical Center are very promising and suggest significant cost saving opportunities and reduced patient boarding times.

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A cost sensitive inpatient bed reservation approach to reduce emergency department boarding times

Qiu

Chinnam

Murat

et al. 2014

Health Care Manag Sci

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An ontology-driven approach to mobile data collection applications for the healthcare industry

Henriques

Lamanna

Kotowski

et al. 2013

Netw Model Anal Health Inform Bioinforma

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Technology has often been associated with improvements in many domains. This is particularly true in the medical and healthcare industry. This is a field where data collection is performed on a daily basis. With the advent of mobile technology, several methodologies for data collection have been adopted to reduce the cost and time expended on data collection. The focus of this paper is a proposed ontology-based framework that has the ability to build a shared repository of surveys that can be used for data collection. The paper discusses iCollect, a first instantiation of the framework in the form of a survey application built for the Indigenous Health Adaptation to Climate Change (IHACC) project.

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Dynamic distributed predictive learning models that preserve privacy for hospitals with insufficient labeled data

Mathew

Obradović

2013

Netw Model Anal Health Inform Bioinforma

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Semantic-enhanced models to support timely admission prediction at emergency departments

Cited by 5 publications

References 42 publications

A cost sensitive inpatient bed reservation approach to reduce emergency department boarding times

A cost sensitive inpatient bed reservation approach to reduce emergency department boarding times

An ontology-driven approach to mobile data collection applications for the healthcare industry

Dynamic distributed predictive learning models that preserve privacy for hospitals with insufficient labeled data

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